Attributes:
Visual attributes have been used quite extensively for a variety of tasks such as image classification [1]}, [2]}, [3]}, deeper image understanding [4]}, image search [5]}, [6]}, segmentation [7]} and semi-supervised learning [8]} . They are mid-level semantic (nameable) visual properties such as furry, wooden, young, . They have been shown to be an effective modality for humans and machines to communicate with each other [5]}, [3]}, [6]}, [12]}. We build upon these recent developments and propose attributes as a mode of communication among machines that may otherwise be unable to communicate due to differences in sources of data and sensors. The fact that attributes are semantic ensures that humans annotating the first few labeled images in both domains are annotating the same semantic concept. This provides a common ground between both domains, necessary for communication.
